| Title | Creating a Chinese suicide dictionary for identifying suicide risk on social media |
| Authors | Lv, Meizhen Li, Ang Liu, Tianli Zhu, Tingshao |
| Affiliation | Chinese Acad Sci, Inst Psychol, Key Lab Behav Sci, Beijing 100101, Peoples R China. Peking Univ, Dept Psychol, Beijing 100871, Peoples R China. Beijing Forestry Univ, Dept Psychol, Beijing, Peoples R China. Univ New S Wales, Black Dog Inst, Sydney, NSW, Australia. Peking Univ, Inst Populat Res, Beijing 100871, Peoples R China. Chinese Acad Sci, Inst Comp Technol, Key Lab Intelligent Informat Proc CAS, Beijing, Peoples R China. |
| Keywords | Weibo Suicide risk Microblog Social media China VALIDITY DEPRESSION NARRATIVES EXPRESSION LANGUAGE |
| Issue Date | 2015 |
| Publisher | PEERJ |
| Citation | PEERJ.2015,3. |
| Abstract | Introduction. Suicide has become a serious worldwide epidemic. Early detection of individual suicide risk in population is important for reducing suicide rates. Traditional methods are ineffective in identifying suicide risk in time, suggesting a need for novel techniques. This paper proposes to detect suicide risk on social media using a Chinese suicide dictionary. Methods. To build the Chinese suicide dictionary, eight researchers were recruited to select initial words from 4,653 posts published on Sina Weibo (the largest social media service provider in China) and two Chinese sentiment dictionaries (HowNet and NTUSD). Then, another three researchers were recruited to filter out irrelevant words. Finally, remaining words were further expanded using a corpus-based method. After building the Chinese suicide dictionary, we tested its performance in identifying suicide risk on Weibo. First, we made a comparison of the performance in both detecting suicidal expression in Weibo posts and evaluating individual levels of suicide risk between the dictionary-based identifications and the expert ratings. Second, to differentiate between individuals with high and non-high scores on self-rating measure of suicide risk (Suicidal Possibility Scale, SPS), we built Support Vector Machines (SVM) models on the Chinese suicide dictionary and the Simplified Chinese Linguistic Inquiry and Word Count (SCLIWC) program, respectively. After that, we made a comparison of the classification performance between two types of SVM models. Results and Discussion. Dictionary-based identifications were significantly correlated with expert ratings in terms of both detecting suicidal expression (r = 0.507) and evaluating individual suicide risk (r = 0.455). For the differentiation between individuals with high and non-high scores on SPS, the Chinese suicide dictionary (t1: F-1 = 0.48; t2: F-1 = 0.56) produced a more accurate identification than SCLIWC (t1: F-1 = 0.41; t2: F-1 = 0.48) on different observation windows. Conclusions. This paper confirms that, using social media, it is possible to implement real-time monitoring individual suicide risk in population. Results of this study may be useful to improve Chinese suicide prevention programs and may be insightful for other countries. |
| URI | http://hdl.handle.net/20.500.11897/435600 |
| ISSN | 2167-8359 |
| DOI | 10.7717/peerj.1455 |
| Indexed | SCI(E) PubMed SSCI |
| Appears in Collections: | 心理与认知科学学院 人口研究所 |
